The application of deep learning models to large-scale data sets requires
means for automatic quality assurance. We have previously developed a fully
automatic algorithm for carotid artery wall segmentation in black-blood MRI
that we aim to apply to large-scale data sets. This method identifies nested
artery walls in 3D patches centered on the carotid artery. In this study, we
investigate to what extent the uncertainty in the model predictions for the
contour location can serve as a surrogate for error detection and,
consequently, automatic quality assurance. We express the quality of automatic
segmentations using the Dice similarity coefficient. The uncertainty in the
model's prediction is estimated using either Monte Carlo dropout or test-time
data augmentation. We found that (1) including uncertainty measurements did not
degrade the quality of the segmentations, (2) uncertainty metrics provide a
good proxy of the quality of our contours if the center found during the first
step is enclosed in the lumen of the carotid artery and (3) they could be used
to detect low-quality segmentations at the participant level. This automatic
quality assurance tool might enable the application of our model in large-scale
data sets.

应用深度学习模型于大规模数据集需要自动质量保证的方法，本文研究了不确定性在模型预测中的应用，并发现不确定性度量可以作为错误检测和自动质量保证的代理，并为大规模数据集提供了自动质量保证工具。